The development and implementation of systems for real-time monitoring of health data has been limited in veterinary medicine by the lack of computerized, automatically collected data. We describe the construction of a surveillance system for early detection of cattle diseases based on laboratory requests, and explore algorithms for automated classification of data into syndromic groups. Classification rules resulted in high accuracy (99.95%), and allowed detailed documentation of the medical knowledge input in the model, improving communication with experts contributing to system development.